1. The Field of the Invention
The present invention relates to electronic messaging, and more specifically, to categorizing electronic messages based on trust between electronic messaging entities.
2. Background and Relevant Art
Computer systems and related technology affect many aspects of society. Indeed, the computer system's ability to process information has transformed the way we live and work. Computer systems now commonly perform a host of tasks (e.g., word processing, scheduling, and database management) that prior to the advent of the computer system were performed manually. More recently, computer systems have been coupled to one another to form both wired and wireless computer networks over which the computer systems can communicate electronically to share data. As a result, many tasks performed at a computer system (e.g., voice communication, accessing electronic mail, electronic conferencing, web browsing) include electronic communication with one or more other computer systems via wired and/or wireless computer networks.
In particular, electronic mail has become an important method for communicating. To send an electronic mail message, a sending user typically manipulates an input device, such as a keyboard, within an active electronic mail application to enter text into an electronic mail message. The sending user also typically includes an electronic mail address of a recipient user in the electronic message, for example, by entering text in the “To” field. The sending user then sends the electronic mail message to the recipient user by selecting a “Send” control within the active electronic mail application. Sending the electronic message may cause the electronic mail message to be routed from the sending user's computer system, through a sending mail server, across a network, to a receiving mail server that stores electronic mail messages for the recipient user.
To view the electronic mail message, the recipient user establishes a connection from an electronic mail client application to the receiving mail server. Establishing the connection typically causes all electronic mail messages sent to the recipient user, including the mail message from the sending user, to be transferred from the receiving mail server to the recipient user's computer system. After the electronic mail message from the sending user is transferred, the recipient user may manipulate an input device, such as, for example, a mouse to view the electronic mail message. Manipulating an input device often includes selecting an identifier (e.g., an icon), which represents the electronic mail message, from an “inbox” folder. When the identifier is selected the full text of the electronic message may become viewable to the recipient user.
Sending an electronic mail message to a recipient user is a relatively easy task, as all that is needed to route the electronic mail message is an electronic mail address of the recipient user. Further, most electronic mail applications allow a sending user to easily send the same electronic mail message to a number of recipient users by including multiple electronic mail addresses in the “To” field. Some electronic mail applications even include the functionality to configure a computer system to automatically send an electronic message to multiple recipient users without human intervention. Such computer systems are often configured to “mass mail” advertisements or marketing materials to large numbers of electronic mail addresses. These computer systems are also often configured to send mass mailings to recipient users even if the recipient users have made no request to receive the mass mailing.
Thus, at times, recipient users may receive unsolicited and potentially unwanted electronic mail messages containing advertisements and marketing material. Most recipient users view these types of electronic mail messages as electronic junk mail, and these types of electronic mail messages are often referred to as “SPAM.” Receiving SPAM wastes the time of recipient users since time must be taken to review a received electronic mail message before the received electronic mail message can be identified as SPAM. Once identified as SPAM, additional time must be taken to delete the received electronic mail message. As such, computerized techniques have been developed to detect SPAM and, after detection, process SPAM differently than other electronic mail messages.
One SPAM detection technique is to use electronic mail filters configured to categorize electronic mail messages, for example, as SPAM, based on the characteristics of the electronic mail messages. Electronic mail filters can use relatively simple algorithms, such as, for example, searching for key words (e.g., ‘$$$’) within an electronic mail message, or relatively complex algorithms, such as, for example, running a Bayesian analysis on an electronic mail message. Electronic mail filters can also be configured to process SPAM in a way that differs from other electronic mail messages. For example, an electronic mail filter can be configured so that electronic mail messages including specific keywords are categorized as SPAM and moved to a SPAM folder.
Further, more sophisticated individually trainable electronic mail filters have also been developed. An individually trainable filter is typically included in a recipient user's computer system. When an electronic mail message is received at the recipient user's computer system, the recipient user provides feedback about the received electronic mail message to the individually trainable filter. The individually trainable filter analyzes the feedback to incrementally generate rules used to categorize subsequently received electronic messages. After a sufficient amount of feedback is analyzed and corresponding rules generated, this eventually results in the individually trainable filter being able to automatically categorize electronic mail messages with little intervention from the recipient user. Accordingly, if properly configured, electronic mail filters can be useful for reducing the amount of SPAM received by a recipient user.
Unfortunately, electronic mail filters suffer from at least two major drawbacks. One drawback is that it is difficult if not impossible to keep electronic mail filters up to date. Entities that send SPAM (hereinafter referred to as “spammers”) continually develop new approaches to attempt defeat known filtering algorithms, such as, for example, altering the arrangement of text in the contents of an electronic mail message. A user of an electronic mail filter may have to frequently check for product updates to maintain the highest level of SPAM protection. Many electronic mail users lack the desire and know how to check for updates with the same frequency spammers develop new approaches to defeating existing filtering algorithms.
Another drawback is that some electronic mail filters require a recipient user to provide significant feedback about received electronic messages. For example, to generate sufficient feedback for an individually trainable electronic mail filter, a recipient user may need to provide feedback on hundreds of electronic messages before the individually trainable electronic mail filter has enough feedback to automatically categorize electronic messages with any degree of accuracy. If a recipient user receives only two or three electronic mail messages a day, it may take weeks or months to generate an appropriate level of feedback.
Another SPAM detection technique is to use electronic mail address blacklists. When an electronic mail address, or even an electronic mail domain, is identified as either sending SPAM or not preventing associated users from sending SPAM, the electronic mail address or electronic mail domain is added to a blacklist. Thereafter, when a recipient user receives an electronic mail message, the recipient user's electronic mail server (or computer system) can check the blacklist to determine if the sender's electronic mail address or electronic mail domain is included in the blacklist. If so, the recipient user's electronic mail server (or computer system) can identify the electronic mail message as SPAM. The recipient user's electronic mail server (or computer system) can then delete the electronic mail message or mark the electronic mail message as SPAM.
Unfortunately, until information associating an electronic mail address with SPAM is indicated, both electronic mail filters and blacklists will identify electronic mail messages from an electronic mail address as legitimate. That is, an electronic mail filter will not identify electronic mail messages as SPAM until received user feedback or updates indicate that electronic messages from a particular electronic mail address are SPAM. Likewise, a blacklist will not identify electronic mail messages as SPAM until received updates indicate that electronic messages from a particular electronic mail address are SPAM. Thus, in the absence of express information indicating that an electronic mail address is associated with SPAM, electronic mail filters and blacklists default to identifying electronic mail messages as legitimate.
Thus, spammers will often intentionally configure electronic mail messages to defeat electronic mail filters and blacklists. For example, when a spammer becomes aware of an electronic mail filter identifying a particular text pattern, the spammer can change the particular text pattern. Similarly, when a spammer's electronic mail address is added to a blacklist, the spammer may simply obtain a new electronic mail address. Other spammers may spoof an electronic mail address or electronic mail domain to defeat existing techniques used to identify electronic mail messages as SPAM. Spoofing changes the domain name of sender's electronic mail address (i.e., the text after the “@” in the electronic mail address) to make it appear as if an electronic mail message was sent from an entity, when that entity did not in fact send the electronic mail message.
Accordingly, when an electronic mail message does not originate from an electronic mail address or domain previously identified as sending SPAM and does not have known characteristics of SPAM, the electronic mail message will typically not be identified as SPAM. Thus, after transfer to a recipient user's inbox, the recipient user is required to spend time manually identifying the electronic mail message as SPAM and appropriately disposing of the electronic mail message. Therefore systems, methods, computer program products, and data structures for identifying unwanted and unsolicited electronic messages in a more proactive manner would be advantageous.